CANCER CHEMOPREVENTION (R AGARWAL, CV RAO AND S YU, SECTION EDITORS)
Molecular Docking, Drug Likeness, and ADMET Analyses of Passiflora Compounds as P-Glycoprotein (P-gp) Inhibitor for the Treatment of Cancer
Serap Yalcin1
Accepted: 8 September 2020
#Springer Nature Switzerland AG 2020
Abstract
Cancer disease leads to deaths worldwide. Anti-cancer drugs have a high prevalence of side effects and cause multidrug resistance (MDR) that remains a significant barrier to major cancer therapy. To date, chemical and herbal substances have been analyzed for their MDR modulatory activity. However, research on new and safe molecules has been continued to overcome MDR in cancer. The plant compounds can be an effective inhibitor for successful cancer therapy. Recently, computational models have gained importance to discover new inhibitors. In the present study, we aimed to explore the various compounds of Passiflora species as P-gp inhibitor. P-gp protein was docked with the active substrate and inhibitor, respectively, including tamoxifen and verapamil. Besides, 3D structure of P- gp was docked with 11 compounds (luteolin, beta amyrin, beta-sitosterol, chimaphilin, chrysin, edulan I and II, apigenin, oleanolic acid, stigmasterol, hydroxyflavone) of plant origin using AutoDock4.2 program. Furthermore, the compounds were analyzed for ADMET and drug likeness properties of compounds determined as Lipinski, Veber, and Ghose’s rules (http://www.swissadme.ch/).
As obtained molecular docking analysis results, luteolin, chrysin, hydroxyflavone, and apigenin may be a candidate for being P-gp inhibitor. Hence, it may be of attention to consider these compounds for further in vitro and in vivo evaluation.
Keywords P-gp inhibitor . Passiflora . Molecular docking . Drug likeness . ADMET
Introduction
Traditional cancer therapies are surgery, radiation therapy, and chemotherapy, or their combinations [1]. Chemotherapy generally is more difficult important in the treatment of met- astatic malignancies and it also causes multiple drug resistance (MDR) and side effects on healthy cells [2]. MDR demon- strates a large field of resistance against functionally and struc- turally unrelated chemotherapeutic agents [3,4], is the ability of cancer cells to escape and to survive from chemotherapeu- tics in cancer therapy, and this situation seriously disrupts the success of cancer chemotherapy [4–7].
ATP-binding cassette transporters (ABC transporters) are a complicated pump superfamily, in which substrate is transported across membranes against a concentration gradient
in the efflux of small molecule drugs [8–11]. P-glycoprotein (P- gp) is one of the well-described ABC transporters which are currently considered to be one of the important barriers in can- cer therapy [12]. P-gp has an important role in drug resistance and its overexpression has been associated with the MDR, so it has become a therapeutic target to overcome MDR [7,9].
Since prehistoric times, flowers, berries, roots, and leaves of herbals have great importance and they have been used in traditional natural medicine, natural products have a key role in the discovery of new drugs, and they have been in constant use in therapy of different diseases [13]. Passiflora species are also one of the natural products. Studies have reported various pharmacological activity of Passiflora species including anti- oxidant [14] and anti-tumor [15] effects.
Recently, computational methods are a rapidly growing area and play an important role in drug discoveries in medicine and therapeutics [16]. Molecular dynamic, pharmacophore modeling, QSAR, and docking analyses can determine protein-ligand inter- action, structural changes, binding sites, drug candidates, etc.
[17–21]. Prompted by this, in the present study, we investigated new potential inhibitors of P-gp from compounds of Passiflora species with molecular docking analyses.
This article is part of the Topical Collection onCancer Chemoprevention
* Serap Yalcin
1 Faculty of Sciences and Arts, Department of Molecular Biology and Genetics, Kırsehir Ahi Evran University, Kırsehir, Turkey https://doi.org/10.1007/s40495-020-00241-6
/ Published online: 18 September 2020
Material and Method
Molecular Structure PreparingTo analyze the molecular docking between P-gp and potential inhibitors, we used the P-gp structures (PDB code: 6c0v) which were found by Kim and Chen et al. at the resolutions of 3.4 Å. The PDB file for P-gp proteins was obtained from the RCSB Protein Data Bank (available athttp://www.rcsb.
org). The water and other molecules were removed from P-gp protein, and then, only 3D structure of P-gp (Fig.1) was hid- den as pdb. file. 3D structures of 11 ligand molecules, includ- ing luteolin, beta amyrin, beta-sitosterol, chimaphilin, chrysin, edulan I and II, apigenin, oleanolic acid, stigmasterol, hydroxyflavone, and control drugs (tamoxifen and verapa- mil), were detected for molecular docking from Pubchem (https://pubchem.ncbi.nlm.nih.gov/) (Table1).
Molecular Docking Analyses
In this study, we performed AutoDock-Version 4.2 (http://
autodock.scripps.edu) to analyze molecular docking. The AutoDock is designed as computational docking tools for the prediction of protein-ligand interaction [Morris et al.
1998]. Molecular docking calculations were analyzed via Lamarckian Generic Algorithm [22] in Autodock Vina [23, 24]. All bound water molecules and nonprotein molecules were removed from the proteins, non-polar hydrogen atoms were merged, and the polar hydrogen atoms were added. The Molegro Molecular Viewer 2.5 (Molegro Molecular viewer
academic free software) and VMD (Visual Molecular Dynamic) [24] programs were used in the visualization of protein-ligand interaction [25].
Drug Likeness and ADME Analysis
Recently, in silico ADMET analyses are gaining attention in computer-based drug discovery [26]. ADMET analyses are used to determine the pharmacological structure from the per- spective of drug discovery (http://biosig.unimelb.edu.au/
pkcsm/prediction). Pharmacokinetics and drug likeness prediction for compounds were also performed by online tool SwissADME (http://www.sib.swiss) (http://www.
s w i s s a d m e . c h / i n d e x . p h p) [2 7, 2 8] . I n a d d i t i o n , pharmacokinetics and drug likeness predictions have been applied on Lipinski, Ghose, and Veber rules and bioavailability scores [29–31].
Results and Discussion
Cancer is a complex disease, and multiple drug resistance is a major drawback in cancer therapy. Therefore, the design and development of new drugs are becoming increasingly neces- sary. P-gp is a significant factor of MDR because its overex- pression is associated with increased efflux of cancer drugs in cancer [10]. Here, we aimed at the discovery of new drug compounds with computer-based analyses and presented an opportunity for further experimental analysis.
Fig. 1 3D structure of P-gp
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Table 1 Ligands used in the study and their properties
No Ligands PubChem ID code
Molecular weight (g.mol
-1)
Structure(2D) Structure(3D)
1 Luteolin 5280445 286.24 g/mol
2 Beta -Amyrin 73145 426.7 g/mol
3 Beta-Sitosterol 222284 414.7 g/mol
4 Chimaphilin 101211 186.21 g/mol
5 Chrysin 5281607 254.24 g/mol
Table 1 (continued)
6 Edulan I 521066 192.3 g/mol
7 Edulan II 6432428 192.3 g/mol
8 Apigenin 5280443 270.24 g/mol
9 Oleanolic Acid 10494 456.7 g/mol
10 Stigmasterol 5280794 412.7 g/mol
11 Hydroxyflavon e
72279
238.24 g/mol
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The search for herbal compounds cannot be easy to use them for experiments in vitro and in vivo. Recently, predicted data of these compounds were obtained by applying computer-based studies. The absorption, distribution, metabolism, elimination, and toxicity (ADMET) analysis have a big importance in drug discovery studies. In silico ADMET predictions have been de- signed to evaluate the pharmacokinetic and toxicity properties. In present work, human intestinal absorption, aqueous solubility
levels, BBB penetration levels, CYP inhibition, hepatotoxicity, etc. of luteolin, beta amyrin, beta-sitosterol, chimaphilin, chrysin, edulan I and II, apigenin, oleanolic acid, stigmasterol, hydroxyflavone, and control drugs (tamoxifen and verapamil) were determined. ADMET and pharmacokinetics results are pre- sented in the supplementary file (supplementary data). ADMET analysis shows that most of the compounds are predicted good human intestinal absorption, no toxicity, and water solubility.
Table 1 (continued)
12 Tamoxifen 2733526 371.5 g/mol
13 Verapamil 2520 454.6 g/mol
Table 2 A drug likeness results of potential inhibitors
Ligand Drug likeness Bioavailability Score
Lipinski Ghose Veber
Luteolin Yes Yes Yes 0.55
Beta-amyrin Yes 1 violation: MLOGP > 4.15 No 3 violations: WLOGP > 5.6, MR > 130, #atoms > 70 Yes 0.55 Beta-sitosterol Yes 1 violation: MLOGP > 4.15 No 3 violations: WLOGP > 5.6, MR > 130, #atoms > 70 Yes 0.55
Chimaphilin Yes Yes Yes 0.55
Chrysin Yes Yes Yes 0.55
Edulan I Yes Yes Yes 0.55
Edulan II Yes Yes Yes 0.55
Apigenin Yes Yes Yes 0.55
Oleanolic acid Yes 1 violation: MLOGP > 4.15 No 3 violations: WLOGP > 5.6, MR > 130, #atoms > 70 Yes 0.56 Stigmasterol Yes 1 violation: MLOGP > 4.15 No 3 violations: WLOGP > 5.6, MR > 130, #atoms > 70 Yes 0.55
Hydroxyflavone Yes Yes Yes 0.55
In addition, drug likeness results of potential inhibitors are shown in Table2. According to Lipinski’s rule (Pfizer’s rule, Lipinski’s rule of five, RO5), the active drug has no more than one violation of the following properties including molecular weight (MW)≤500, LogP≤5, hydrogen bond acceptors≤10, and hydrogen bond donors≤5 [29]. According to Veber rules, the active drug has total hydrogen bonds≤12, rotatable bonds
≤10, and polar surface area (PSA). Polar surface area≤140 tend to have oral bioavailability≥20% [30]. According to Ghose rules, active drug has Log P(−0.4~5.6), MR (molar refractivity (40~150), MW (160~480), number of atoms (20~70), and polar surface area (PSA) < 140 [31]. Based on the drug likeness analysis, all the compounds were found by the Lipinski’s and Veber rule. Furthermore, luteolin, chimaphilin, chrysin, edulan I, edulan II, apigenin, and hydroxyflavone complied with Ghose’s rules.
To better understand interaction with P-gp of luteolin, beta amyrin, beta-sitosterol, chimaphilin, chrysin, edulan I and II, apigenin, oleanolic acid, stigmasterol, and hydroxyflavone compounds, a molecular docking analysis
was performed by Autodock-Vina program. For this pur- pose, tamoxifen and verapamil were selected as reference drugs. The general properties of molecules are described in Table 1. The results of molecular docking analyses of 11 compounds and the number of hydrogen bonds are summarized in Tables3 and4. In the procedure, luteolin, beta amyrin, beta-sitosterol, chimaphilin, chrysin, edulan I and II, apigenin, oleanolic acid, stigmasterol, and hydroxyflavone were docked to the proteins with a bind- ing free energy of−10.7,−10.0,−8.7,−6.8,−8.6,−6.2,
−7.5,−8.1,−8.9,−8.6, and−8.7 kcal mol−1, respective- ly. For P-gp [32] protein and luteolin interaction, five hy- drogen bonds were identified with amino acid residue Thr 1174, Phe 904, Arg 905, Asp 167, and Val 168. In human, the maximum number of hydrogen bond interactions was detected between luteolin and P-gp protein. In the P-gp protein and apigenin interaction, hydrogen bonds can be observed with residue Tyr 1044, Ser 1077, and Lys 1076.
Hydrogen bonds of other ligands and P-gp interaction are shown in Table 4.
Table 3 Protein-ligand molecular docking results
Protein Ligand Binding Energy (kcal/mol)
Interaction
P-gp
Luteolin -10.7
kcal/mol
P-gp
Beta -Amyrin -10.0 kcal/mol
P-gp
Beta-Sitosterol -8.7 kcal/mol
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Table 3 (continued)
P-gp
Chimaphilin -6.8 kcal/mol
P-gp
Chrysin -8.6
kcal/mol
P-gp
Edulan I -6.2
kcal/mol
P-gp
Edulan II -7.5 kcal/mol
P-gp
Apigenin -8.1
kcal/mol
Table 3 (continued)
P-gp
Oleanolic Acid -8.9 kcal/mol
P-gp
Stigmasterol -8.6 kcal/mol
P-gp
Hydroxyflavone -8.7 kcal/mol
P-gp
Tamoxifen -8.8 kcal/mol
P-gp
Verapamil -6.8
kcal/mol
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Table 4 Hydrogen bonds between ligands and P-gp protein
Protein Ligand H
bound
Ligand-protein interaction
P-gp
Luteolin 5
P-gp
Beta -Amyrin 0
P-gp
Beta-Sitosterol 0
P-gp
Chimaphilin 0
P-gp
Chrysin 2
P-gp
Edulan I 0
Table 4 (continued)
P-gp Edulan II 0
P-gp Apigenin 3
P-gp Oleanolic Acid 0
P-gp Stigmasterol 0
P-gp Hydroxyflavone 1
P-gp Tamoxifen 0
P-gp Verapamil 2
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Conclusion
The objective of this work was to obtain and evaluate molec- ular docking, predicted drug likeness, and ADMET analyses in potential compounds of Passiflora species. The binding energies, ADMET, and drug likeness for ligands were com- pared with the control drug, tamoxifen, and verapamil. As a result, luteolin, chrysin, apigenin, and hydroxyflavone may be potential inhibitors for P-gp and be helpful in cancer therapy.
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